1. Field of the Invention
This invention concerns data compression in general and specifically to seismic data compression.
2. Description of the Prior Art
Seismic data is collected for monitoring of earthquakes and energy explorations and is collected by a series of seismometers placed at different locations. Seismic exploration of the earth's crust is used extensively not only on land but also offshore to identify oil and gas deposits in subterranean earth formations.
The motivation of compressing seismic data is that it takes enormous storage space and demands considerable transmission bandwidth to transfer the data from a seismic measuring location to a central processing location. Also for many situations where real time data processing is required, data compression is the key since it is possible only if the data are transmitted very quickly. In a modern 2D seismic acquisition survey, in which data is acquired by generating a loud sound at one location and recording at a receiver the resulting vibrations in the earth at another location, up to 100 terabytes of data may be recorded. That number increases in the range of multiple hundreds of terabytes due to acquisition of 3D or 4D surveys instead of a traditional 2D one, and emerging new techniques in data acquisition such as wide azimuth and full azimuth acquisitions. Therefore, seismic data compression is desirable in order to save storage space and transmission time.
For data compression, widely accepted metrics for measuring compression efficiency are defined as,
      compression    =                  Uncompressed        ⁢                                  ⁢        Size                    Compressed        ⁢                                  ⁢        Size                        compression      ⁢                          ⁢      ratio      ⁢                          ⁢              (        CR        )              =                  Compressed        ⁢                                  ⁢        Size                    Uncompressed        ⁢                                  ⁢        Size            
These two measures are reciprocal to each other. The compression ratio is usually shown in percentage.
For seismic data compression methods such as the wavelet based approaches, data loss can occur such that the decompressed data is usually slightly different from the original data. The difference between them (i.e., data residual) is a measure of compression quality. For the same compression quality, the larger the compression, the better compression that results.
There have been many approaches regarding seismic data compression. Different transformations, filters and coding schemes have been used to achieve great compression but with a certain level of precision. These techniques exploit similarities of different pieces of seismic data, find approximate image partitions, condense information, and reduce statistical redundancy. However, there is hardly any absolute repetition (of the entire 32-bit vector) even if millions of different data traces are analyzed. In order to maintain zero precision loss, those differences cannot be simply ignored or rounded. Lossless compression methods have also been proposed, and most of them have used linear prediction as the major step. But they either require huge memory or high computational complexity.
In the code compression for embedded systems area, there is an efficient and lossless compression technique called the bitmask-based method. It uses dictionary-based compression to exploit existing repetitions as well as bitmask-matched repetitions by remembering the differences. Due to the similar precision requirement in seismic data compression, the bitmask-based approach was once expected to be very promising. Unfortunately, as reported in the CISE Technical Report 08-452, Wang, Mishra, University of Florida, 2008, direct application of the bitmask-based technique resulted in poor compression primarily due to the fact that there is rarely useful repetition in seismic data.
To obtain higher compression performance, wavelet-based methods, which are lossy, are usually adopted, mainly because wavelet-based methods can provide good localization in both time and frequency, resulting in the energy compact which is essential for a high compression ratio. Wavelet-based data compression has been an active research topic in the last two decades: for example,
Coffman et al., Wavelet Analysis and Signal Processing; Proceedings of the International Conference “Wavelets and Applications”, Y. Meyer and S. Rogues, editors, Toulouse, France, 1992, Pages 77-93, 1992;
Devore et al., Image compression through wavelet transform coding. IEEE Transactions on Information Theory, 38:719-746, 1992;
Lu et al., Comparative study of wavelet image coders, Optical Engineering, 35(9), 2605-2619, 1996; and
Mathieu et al., Compression d'images par transformee en ondelette et quantification vectorielle. Traitment du Signal, 7(2) :101-115, 1990.
The history of wavelet-based seismic data compression can be dated back to mid-1990, for example,
Donoho et al., High-performance seismic trace compression. In Proceedings of SEG, p. 160-163, 1995; and
Reiter and Hall, A comparison of multi-dimensional wavelet compression methods. In Proceedings of EAGE, 1996.
Seismic data compression presents more difficulties than still image compression, due to certain image discontinuities (e.g., residual statics) and large amplitude imbalances. So far, various wavelet based seismic data compression methods have achieved different compression ratios, and has been used for data visualization, data transmission as well as seismic imaging algorithms, among other possible applications.
3. Identification of Objects of the Invention
In the past, most wavelet-based seismic data compression algorithms have concentrated on either different wavelet types as basis wavelets or on the different encoding/quantization approaches in compression. It is an object of this invention to take advantage of the fact that the seismic signals in one shot gather actually all evolve from one common source wavelet in an active seismic survey.